• NEANIAS Space Research Community

A dataset of simulated astronomical radio maps (fits format, size 2500 x 2500) with both compact and extended sources that can be used to test automated source extraction algorithms. Maps were produced with the CASA tool as described in S Riggi et al, PASA (2019) to test CAESAR source finding performances. The dataset includes: - Simulated maps in FITS format ("recmap_vis-RUNXXX.fits") - List of generated sources in Caesar ROOT format ("sources-RUNXXX.root") and corresponding DS9 regions ("ds9regions-RUNXXX.reg") - List of generated sources (convolved by the clean beam) in Caesar ROOT format ("sources-RUNXXX_conv_rec.root") and corresponding DS9 regions ("ds9regions_sources-RUNXXX_conv.reg") {"references": ["S. Riggi et al, \"Automated detection of extended sources in radio maps: progress from the SCORPIO survey\", MNRAS 460, 1486-1499 (2016)", "S. Riggi et al, \"CAESAR source finder: recent developments and testing\", PASA 2019"]}

Weights files (.h5) of caesar-mrcnn source finder model at different training epochs (150, 250).

This is a data set that accompanies the paper "Compact continuum source finding for next generation radio surveys" (2012MNRAS.422.1812H) The image files and source catalogues contained here were used to test the completeness and false detection rate of a number of source finding algorithms including: Aegean, Selavy, Sfind, SExtractor, and IMSAD. These data can be used to assess the performance of future source finding codes, and to verify the the performance of code during development.

Q fever, caused by the bacterium C. burnetii, is a zoonosis with a worldwide distribution that affects both humans and animals. From 2007 to 2010, large community outbreaks of Q fever were observed in the Netherlands. In 2008 and 2009 source finding investigations were initiated by several Municipal Health Services, primarily on commercial dairy goat farms, to pinpoint potential sources for Q fever. In that same year, the Food and Consumer Product Safety Authority initiated a project to investigate if petting zoos were a potential source for human Q fever as well. Petting zoos showed insufficient C. burnetii DNA content for molecular typing (qPCR Cq values higher than 33) and were not considered an important source for human Q fever. However, 31 samples from eight out of 57 commercial dairy farms, involved in source finding investigations in 2009, showed a relatively high C. burnetii DNA content based on qPCR data for single copy target com1 (Cq values lower than 33). These samples were selected for molecular typing using a Multi-Locus Variable number of tandem repeat Analysis (MLVA). In this study we show that samples highly positive for C. burnetii DNA can be successfully typed using a multiplex MLVA assay. Three different MLVA types were found, based on six MLVA markers. On seven out of eight locations a single MLVA type was found. On one location a mixture of two types was observed within a number of samples. Our findings show that multiple MLVA types are present in the Netherlands, which is promising for future source finding investigations to identify potential sources. However, only a few different MLVA types have been determined in human and animal samples so far, which makes identifying transmission routes and sources of C. burnetii in the Netherlands still challenging.

Star formation is a complex hierarchical process that witnesses the transfer of mass among a range of scales from large diffuse molecular clouds to crowded clumps and finally down to prestellar cores. The final stage of this process has prestellar cores actively accreting matter while undergoing gravitational collapse on their way to becoming main sequence stars. This thesis presents multi wavelength submillimeter observations of the Perseus molecular cloud using 160 μm, 250 μm, 350 μm, and 500 μm maps of thermal dust emission from the Herschel space observatory. Additionally C18O J = 3 → 2 spectral line emission is observed in four star forming clumps within Perseus using the James Clerk Maxwell Telescope. Spectral line emission allows for the separation of material along the line of sight. Prestellar core mass is derived from observational maps using various source finding algorithms. The mass is overestimated when compared to prestellar core mass found from spectral line data. This overestimation can be mitigated with careful selection of source finding algorithm and background removal. Further, the prestellar core mass derived from spectral line data was the closest match to the initial stellar mass function over dust maps. However, both the spectral line masses and dust map masses do not agree with the IMF confirming a star forming efficiency factor in the evolutionary step between prestellar core and main sequence star. Lastly, a filamentary analysis finds that high mass stars preferentially form in crowded regions close to, or contained within, filament structure. Thesis Master of Science (MSc) Star formation is a complex hierarchical process that witnesses the transfer of mass among a range of scales from large diffuse molecular clouds to crowded clumps and finally down to prestellar cores. The final stage of this process has prestellar cores actively accreting matter while undergoing gravitational collapse on their way to becoming main sequence stars. This thesis presents multi wavelength submillimeter observations of the Perseus molecular cloud using 160 μm, 250 μm, 350 μm, and 500 μm maps of thermal dust emission from the Herschel space observatory. Additionally carbon monoxide spectral line emission is observed in four star forming clumps within Perseus using the James Clerk Maxwell Telescope. Spectral line emission allows for the separation of material along the line of sight. Prestellar core mass is derived from observational maps using various source finding algorithms. The mass is overestimated when compared to prestellar core mass found from spectral line data. This overestimation can be mitigated with careful selection of source finding algorithm and background removal. Further, the prestellar core mass derived from spectral line data was the closest match to the initial stellar mass function over dust maps. However, both the spectral line masses and dust map masses do not agree with the IMF confirming a star forming efficiency factor in the evolutionary step between prestellar core and main sequence star. Lastly, a filamentary analysis finds that high mass stars preferentially form in crowded regions close to, or contained within, filament structure.

Finding and classifying astronomical sources is key in the scientific exploitation of radio surveys. Source-finding usually involves identifying the parts of an image belonging to an astronomical source, against some estimated background. This can be problematic in the radio regime, owing to the presence of correlated noise, which can interfere with the signal from the source. In the current work, we present ConvoSource, a novel method based on a deep learning technique, to identify the positions of radio sources, and compare the results to a Gaussian-fitting method. Since the deep learning approach allows the generation of more training images, it should perform well in the source-finding task. We test the source-finding methods on artificial data created for the data challenge of the Square Kilometer Array (SKA). We investigate sources that are divided into three classes: star forming galaxies (SFGs) and two classes of active galactic nuclei (AGN). The~artificial data are given at two different frequencies (560~MHz and 1400~MHz), three total integration times (8 h, 100 h, 1000 h), and three signal-to-noise ratios (SNRs) of 1, 2, and 5. At~lower SNRs, ConvoSource tends to outperform a Gaussian-fitting approach in the recovery of SFGs and all sources, although at the lowest SNR of one, the better performance is likely due to chance matches. The~Gaussian-fitting method performs better in the recovery of the AGN-type sources at lower SNRs. At~a higher SNR, ConvoSource performs better on average in the recovery of AGN sources, whereas the Gaussian-fitting method performs better in the recovery of SFGs and all sources. ConvoSource usually performs better at shorter total integration times and detects more true positives and misses fewer sources compared to the Gaussian-fitting method; however, it detects more false positives. 29 pages, 18 figures, Accepted by Galaxies